Comparison between measured groundwater...
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Study area : the Kairouan plain (central Tunisia) Conclusion Comparison between measured groundwater withdrawals for irrigation and estimated crop water consumption using remote sensing in the Kairouan plain Fradi Fajr 1* , Massuel S. 2 , Simonneaux V. 3 , Calvez R. 2 , Oueslati I. 1 1 Université de Carthage/ INAT, Tunisie; 2 UMR G-EAU, France; 3 CESBIO, France E-mail: [email protected] Introduction Approaches • Mean Rainfall rate: 300mm/year (irregular); • Mean ET 0 : 1600 mm/year; • Abundance of irrigated agriculture; • Common Farm type: mixed (vegetables + field crops + trees); • Main irrigation source : groundwater; • Irrigated perimeters status: public and private; IB01 Total area: 26 ha Crops: • 2013: 56% field crops + 41% chili • 2014: 56% field crops + 16% chili IB02 Total area: 41 ha Crops: • 2013: 39% field crops+ 20% vegetables + 7% olives • 2014: 34% field crops+ 22% vegetables+ 7% olives IB00 Total area: 38 ha Crops: • 2013: 63% olives + 17% peach • 2014: 63% olives + 37% peach March 2014 March 2013 IB00 : the “tree” farm What is compared? Central Tunisia is predominantly semi-arid, characterized by a low rate of rainfall, intermittent surface water and high potential evapotranspiration rate. These factors encourage the use of groundwater exploitation for the development of irrigated agriculture. In the Kairouan plain, the strong irrigated agriculture development leads to intensive exploitation of the aquifers and contribute to the water level drop. Up to know, the water management plans cannot rely on accurate estimations of the groundwater draft from the large number of individual farms. The objective was to estimate the crop water consumption based on the FAO-56 method coupled with satellite estimates of crop coefficients and to compare it with measured pumped volumes at farm scale. This first step should provide insights into irrigation strategies of the farmers in the Kairouan plain. General context In this work, we deal with private irrigated perimeters. VS SAMIR tool ( Satellite Monitoring of Irrigation) Temperature monitoring + discharge measurement Our approach consists of a comparison between an observed irrigation schedule (groundwater withdrawals measured) and theoretical ones (SAMIR tool simulations). Simulated Crop water requirements Groundwater draft measurements A transducer records the temperature of the pipe every 15min. The pumping periods are identified by a change in temperature getting close to GW The discharge is measured periodically with an ultrasonic flowmeter. The SAMIR tool uses basal crop coefficients (Kcb) from satellite NDVI values. SAMIR computes at the daily step the crop evapotranspiration, upadates the soil water content and estimates irrigation inputs from water budget. SAMIR simulation Crop proprieties (root depth, sowing date…) Spatial Data (NDVI) Kcb = a* NDVI +b Soil parameters (Layers depths, diffusions between layers…) Climatic data (Rainfall, ET 0 ) Irrigation parameters (Fw, water depth,%TAW, Kcb start/ stop irrigation) Soil water balance Irrigation schedule In this work, our objective was to see if models based on remote sensing image time series were able to estimate irrigation consumption at farm scale. Due to uncertainties in the model parameters, we choose to test various scenarios from the minimum to the maximum possible water consumption. The results show strong differences in simulated irrigation inputs using these scenarios, but it is clear that compared to simulated crop requirements, strong periodical over-irrigation occurred for some farms. This suggests that the pumping rates are significantly driven by other features than the crop water requirement. Further research is needed to understand the reasons of what could be interpreted as a waste of water. In addition, the results highlight the strong variability in irrigation practices. • Season 2013: overall coherence between observation and simulations. Over-irrigation observed in May can be due to the budding period (critical period for olives); • Season 2014: slightly lower inputs for both simulations and observations is due to the higher rainfall. However SAMIR decreases inputs after the strong rain in March, but the farmer doesn’t. Acknowledgments: We are grateful to the CNES for supplying image time series in the frame of the SPOT4-Take5 experiment, and also for helping us acquiring SPOT5 images thanks to several ISIS actions. We also thank the Tunisian Ministry of Research for granting students involved in this study. Financial support from the MISTRALS/SICMED program for the ReSAMEd project, from the CNES/TOSCA program for the EVA2IRT project and from the ANR/TRANSMED program for the AMETHYST project (ANR-12-TMED- 0006-01) are gratefully acknowledge. • Season 2013 + 2014: the observed pumping rate is stable and decrease only during the wet winter while there are large variations of crop water demand in the period (SAMIR’s supply varies according to these variations). The maximum capacity of the pumping gear prevent the farmer from supplying water according to the pic demand. • Season 2013: maximum irrigation is simulated for vegetables until July. Then, a large difference between observed and simulated irrigations is observed at the end of the summer time; • Season 2014: observed inputs are much higher than simulated ones. Again the pumping capacity is used at its maximum, decreasing only during the wet winter. IB01 : the “field crops” farm IB02 : the “mixed” farm Results: monthly pumped volumes for irrigation Observed irrigation schedule which are the groundwater withdrawals since the measured well irrigates only the studied farm. Observed pumping volumes Irrigation schedule simulated according to parameters calibrated on the Kairouan plain in previous studies (Simonneaux et al., 2009; Saadi et al., 2015); SAMIR medium parameters The models parameters (Kcb, irrigation parameters, etc.) are set to produce maximum irrigation amount SAMIR maximum irrigation The models parameters (Kcb, irrigation parameters, etc.) are set to produce minimum irrigation amount SAMIR economic irrigation Deg.C pumping pumping pumping Groundwater temperature Temperature logger t Q V n ni i pump Pumping Discharge Q1 Volume Pumping Pumping Pumping Discharge Q1 Pumping Discharge Q1 Discharge Q2 Discharge Q2 Time t Q t1 t2 t3 t4 t5 Ultrasonic flowmeter August 2013 May 2013 April 2014 January 2014 March 2013 April 2014
Transcript of Comparison between measured groundwater...
Study area : the Kairouan plain (central Tunisia)
Conclusion
Comparison between measured groundwater withdrawals for irrigation and estimated crop
water consumption using remote sensing in the Kairouan plain
Fradi Fajr 1* , Massuel S.2 , Simonneaux V.3 , Calvez R.2 , Oueslati I.1
1 Université de Carthage/ INAT, Tunisie; 2 UMR G-EAU, France; 3 CESBIO, FranceE-mail: [email protected]
Introduction
Approaches
• Mean Rainfall rate:
300mm/year (irregular);
• Mean ET0 : 1600 mm/year;
• Abundance of irrigated agriculture;
• Common Farm type: mixed
(vegetables + field crops + trees);
• Main irrigation source : groundwater;
• Irrigated perimeters status: public and
private;
IB01Total area: 26 ha Crops: • 2013: 56% field crops + 41% chili• 2014: 56% field crops + 16% chili
IB02 Total area: 41 ha Crops: • 2013: 39% field crops+ 20% vegetables + 7% olives• 2014: 34% field crops+ 22% vegetables+ 7% olives
IB00 Total area: 38 haCrops: • 2013: 63% olives + 17% peach• 2014: 63% olives + 37% peach
March 2014March 2013
IB00 : the “tree” farm
What is compared?
Central Tunisia is predominantly semi-arid, characterized by a low rate of rainfall, intermittent surface water and high potential evapotranspiration rate. These factors encourage the use ofgroundwater exploitation for the development of irrigated agriculture. In the Kairouan plain, the strong irrigated agriculture development leads to intensive exploitation of the aquifers andcontribute to the water level drop. Up to know, the water management plans cannot rely on accurate estimations of the groundwater draft from the large number of individual farms.
The objective was to estimate the crop water consumption based on the FAO-56 method coupled with satellite estimates of crop coefficients and to compare it with measured pumpedvolumes at farm scale. This first step should provide insights into irrigation strategies of the farmers in the Kairouan plain.
General context
In this work, we deal with private irrigated perimeters.
VS
SAMIR tool( Satellite Monitoring of Irrigation)
Temperature monitoring + discharge measurement
Our approach consists of a comparison between an observed irrigation schedule
(groundwater withdrawals measured) and theoretical ones (SAMIR tool simulations).
Simulated Crop water requirements Groundwater draft measurements
A transducer records the temperature ofthe pipe every 15min. The pumping periodsare identified by a change in temperaturegetting close to GW
The discharge is measured periodically with anultrasonic flowmeter.
The SAMIR tool uses basal crop coefficients(Kcb) from satellite NDVI values. SAMIR computesat the daily step the crop evapotranspiration,upadates the soil water content and estimatesirrigation inputs from water budget.
SAMIR simulation
Crop proprieties
(root depth, sowing date…)
Spatial Data
(NDVI)Kcb = a* NDVI +b
Soil parameters
(Layers depths, diffusions between
layers…)
Climatic data
(Rainfall, ET0 )
Irrigation parameters
(Fw, water depth,%TAW, Kcb
start/ stop irrigation)
Soil water balance
Irrigation schedule
In this work, our objective was to see if models based on remote sensing image time series were able to estimate irrigationconsumption at farm scale. Due to uncertainties in the model parameters, we choose to test various scenarios from the minimum to themaximum possible water consumption. The results show strong differences in simulated irrigation inputs using these scenarios, but it isclear that compared to simulated crop requirements, strong periodical over-irrigation occurred for some farms. This suggests that thepumping rates are significantly driven by other features than the crop water requirement. Further research is needed to understand thereasons of what could be interpreted as a waste of water. In addition, the results highlight the strong variability in irrigation practices.
• Season 2013: overall coherence between observationand simulations. Over-irrigation observed in May can bedue to the budding period (critical period for olives);
• Season 2014: slightly lower inputs for both simulationsand observations is due to the higher rainfall. HoweverSAMIR decreases inputs after the strong rain in March,but the farmer doesn’t.
Acknowledgments: We are grateful to the CNES for supplying image timeseries in the frame of the SPOT4-Take5 experiment, and also for helping usacquiring SPOT5 images thanks to several ISIS actions. We also thank theTunisian Ministry of Research for granting students involved in this study.Financial support from the MISTRALS/SICMED program for the ReSAMEdproject, from the CNES/TOSCA program for the EVA2IRT project and fromthe ANR/TRANSMED program for the AMETHYST project (ANR-12-TMED-0006-01) are gratefully acknowledge.
• Season 2013 + 2014: the observed pumping rate is stableand decrease only during the wet winter while there arelarge variations of crop water demand in the period(SAMIR’s supply varies according to these variations). Themaximum capacity of the pumping gear prevent thefarmer from supplying water according to the picdemand.
• Season 2013: maximum irrigation is simulated forvegetables until July. Then, a large difference betweenobserved and simulated irrigations is observed at the endof the summer time;
• Season 2014: observed inputs are much higher thansimulated ones. Again the pumping capacity is used at itsmaximum, decreasing only during the wet winter.
IB01 : the “field crops” farm IB02 : the “mixed” farm
Results: monthly pumped volumes for irrigation
Observed irrigation schedule which are
the groundwater withdrawals since the
measured well irrigates only the studied
farm.
Observed pumping volumes
Irrigation schedule simulated according
to parameters calibrated on the Kairouan
plain in previous studies (Simonneaux et
al., 2009; Saadi et al., 2015);
SAMIR medium parameters
The models parameters (Kcb, irrigation
parameters, etc.) are set to produce
maximum irrigation amount
SAMIR maximum irrigation
The models parameters (Kcb, irrigation
parameters, etc.) are set to produce
minimum irrigation amount
SAMIR economic irrigation
Deg.C
pumping
pumpingpumping
Groundwater
temperature
Temperaturelogger
tQV nni
ipump
Pumping
Discharge Q1
Volume
Pumping Pumping Pumping
Discharge Q1
Pumping
Discharge Q1 Discharge Q2 Discharge Q2
Time
tQ
t1 t2 t3 t4 t5
Ultrasonicflowmeter
August 2013May 2013 April 2014 January 2014March 2013 April 2014
